Deep Reinforcement Learning for Adaptive Dental Treatment Planning

Abstract

This study presents a deep reinforcement learning (DRL) framework for adaptive dental treatment planning based on patient feedback. Traditional treatment planning in dentistry often relies on expert knowledge and manual adjustments, leading to suboptimal outcomes due to variations in patient responses and preferences. The proposed DRL approach leverages patient feedback to continuously adapt treatment plans, optimizing outcomes and improving patient satisfaction. We demonstrate the effectiveness of our framework through simulations and discuss its potential impact on the future of dental care.